Data collection and analysis have become vital organizational tasks in today's highly connected economy. One particular area of collection and analysis is demand forecasting. Organizations use demand forecasts to allocate resources, schedule purchases related to inventory, plan revenue, plan expenses, plan inventory, and for a variety of other reasons.
Large organizations may collect data which is processed by demand forecasting utilities for a multitude of each organization's individual stores, products, etc. That data is typically centrally stored by each organization in a database or data warehouse for all of the organization's stores, products, etc. Consequently, when demand forecasts are needed for specific stores, specific products, etc., the processing load and processing timeliness can become problematic, since the volume of the collected data can be daunting.
To deal with this problem, organizations have developed elaborate schedules and techniques for more efficiently processing and acquiring demand forecasts. For example, an organization may set time aside in the evening during a particular day or the week during which their demand forecast utilities can have exclusive access to their data store and one or more processing nodes. As another example, the organization may require that any particular store or individual requiring a demand forecast submit a request for processing that includes a preset time lag, which gives the organization time to schedule and execute the appropriate forecast utilities.
Yet, even with these and other manual and semi-automated techniques and procedures, the control associated with processing forecast utilities for large amounts of data remains closely guarded and controlled by organizations; because of the processing loads and the resources needed to execute these forecast utilities.
Consequently, departments or individuals affected by demand forecasts become frustrated by what they perceive to be incompetence or excessive bureaucracy within their organizations. These individuals may consume even more time and resources of the organization by organizing design teams of developers or project managers in order to attempt to streamline the production of demand forecasts.
Therefore, there is a need for improved techniques for producing demand forecasts, such that the processing loads and timeliness associated with demand forecasts production can be improved.